Gender: Non-binary 
Age: 18-24
Nationality: United Kingdom
Ethnicity: White / Caucasian;Asian / Pacific Islander 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: Undergraduate degree
Mother's education: Undergraduate degree
Father's education: Doctorate or above
Self-assessed socioeconomic status (1-10): 2
Home: Rent 
Employment: Not employed, but looking for work
Occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Listen to rock/indie music;Exercise;Use social media;Do arts and crafts 
Devices used daily: Laptop;Smartphone;Tablet 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Text-to-Speech (i.e, computers being able to read out loud written language) 
Frequency of LLM use: Rarely
LLMs used: ChatGPT;Other deepseek
LLM use cases: Generic chatbot conversation 
Contexts of LLM use: Personal 
Prompt 1: fusion energy
Prompt 2: how are you?
Prompt 3: fake facts less than 50 characters
Prompt 4: how do i set up a samba share for my rpi nas?